Computational Intelligence Applied to Inverse Problems in Radiative Transfer
This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer. In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by...
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| Övriga upphovsmän: | , , |
| Materialtyp: | Elektronisk E-bok |
| Språk: | engelska |
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Springer International Publishing : Imprint: Springer,
2023.
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| Upplaga: | 1st ed. 2023. |
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| Länkar: | https://doi.org/10.1007/978-3-031-43544-7 |
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| 245 | 1 | 0 | |a Computational Intelligence Applied to Inverse Problems in Radiative Transfer |h [electronic resource] / |c edited by Antônio José da Silva Neto, José Carlos Becceneri, Haroldo Fraga de Campos Velho. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XXXVIII, 228 p. 44 illus.). |b 2 illus. in color. | ||
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| 505 | 0 | |a Foreword -- Preface -- Introduction -- Radiative Transfer -- Inverse Problems in Radiative Transfer: An Implicit Formulation -- Computational Intelligence in Optimization Problems -- Simulated Annealing -- Genetic Algorithms -- Artificial Neural Networks -- Ant Colony Optimization -- Artificial Bee Colony Algorithm -- Particle Swarm Optimization -- Generalized Extremal Optimization -- Particle Collision Algorithm -- Differential Evolution -- Luus-Jaakola Algorithm -- Firefly Algorithm -- Application Projects -- Final Considerations -- References. | |
| 520 | |a This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer. In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies. From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems. While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding. This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Mathematics |x Data processing. | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Computer science. | |
| 650 | 1 | 4 | |a Computational Science and Engineering. |
| 650 | 2 | 4 | |a Mathematical Modeling and Industrial Mathematics. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Optimization. |
| 650 | 2 | 4 | |a Computer Science. |
| 700 | 1 | |a Silva Neto, Antônio José da. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Becceneri, José Carlos. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Campos Velho, Haroldo Fraga de. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031435430 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031435454 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031435461 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-43544-7 |
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| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||